Gemma 4 26B A4B vs Qwen3.7 Plus
Compare Gemma 4 26B A4B and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Models in this comparison
Gemma 4 26B A4B vs Qwen3.7 Plus on Vision Evals
Qwen3.7 Plus scores higher on 3 of the 4 Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.7 Plus leads 60.1% to 44.2%.
Overall, Gemma 4 26B A4B averages 54.1% (#48 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0019 per sample) and faster (7.8s vs 27.8s per sample).
Gemma 4 26B A4B vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Gemma 4 26B A4B | Qwen3.7 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | open | closed |
| Modality | multimodal | — |
| Release Date | Apr 2026 | Jun 2026 |
| Context Window | 256K | — |
| Parameters | 25.2B | Unknown |
| License | Apache 2.0 | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $0.320 |
| Output $/1M | $0.300 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 54.1% 4/5 tasks | 58.9% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0019 | $0.0008 |
| Avg speed / sample | 27.84s | 7.77s |
| By task | ||
| Object Detection | 44.2% ±0.7, Mean of 3 runs, range 43.5 to 44.8 | 60.1% |
| Counting | 43.2% ±2.0, Mean of 3 runs, range 41.9 to 46.0 | 50.0% |
| Identification | 81.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 | 84.4% |
| OCR (low) | – | 60.3% |
| by category |
| |
| OCR (high) | – | 65.5% |
| by category |
| |
| Reasoning (low) | 47.7% ±2.0, Mean of 3 runs, range 45.0 to 49.0 | 39.7% |
| Reasoning (high) | – | 68.2% |
Gemma 4 26B A4B vs Qwen3.7 Plus: Overview
Gemma 4 26B A4B is the Mixture-of-Experts variant in Google's Gemma 4 family, with 25.2B total parameters but only 3.8B active per token. Built from the same Gemini 3 research as the 31B dense sibling and released as open weights under the Apache 2.0 license, it supports a 256K token context window with text and image input and configurable thinking mode. The "A4B" in the name refers to its approximately 4B active parameters. The MoE design makes it significantly faster at inference than the dense 31B, running nearly as fast as a 4B-parameter model while delivering roughly 97% of the dense model's quality.
For vision tasks, the 26B A4B shares the same multimodal capabilities as the 31B image understanding with variable aspect ratios and resolutions, and structured bounding box output for UI element detection. The tradeoff versus the 31B dense model is a small quality reduction in exchange for much faster inference and lower hardware requirements, fitting in 18GB of VRAM at 4-bit quantization. It ranked #6 among open models on the Arena AI text leaderboard at launch.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.7 Plus performed better. It scores higher on 3 of the 4 vision tasks and averages 58.9% (#35 of 61) against 54.1% (#48 of 61) for Gemma 4 26B A4B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.7 Plus leads with 60.1% against 44.2%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0019. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s per inference against 27.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.